class_docstr: Group together categories which occurence is less than a specified
  minimum fraction. Coalesced categories get index of one.
code: def transform(self, X):
      self.check_X(X)
  
      if self.minimum_fraction is None:
          return X
  
      for column in range(X.shape[1]):
          if sparse.issparse(X):
              indptr_start = X.indptr[column]
              indptr_end = X.indptr[column + 1]
              unique = np.unique(X.data[indptr_start:indptr_end])
              for unique_value in unique:
                  if unique_value not in self.do_not_coalesce_[column]:
                      indptr_start = X.indptr[column]
                      indptr_end = X.indptr[column + 1]
                      X.data[indptr_start:indptr_end][
                          X.data[indptr_start:indptr_end]
                          == unique_value
                      ] = 1
          else:
              unique = np.unique(X[:, column])
              unique_values = [
                  unique_value
                  for unique_value in unique
                  if unique_value not in self.do_not_coalesce_[column]
              ]
              mask = np.isin(X[:, column], unique_values)
              X[mask, column] = 1
      return X
  
file_path: auto-sklearn/autosklearn/pipeline/implementations/MinorityCoalescer.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 4 for calling the MinorityCoalescer obj's underlying member method code with example usage: obj.transform(X) and returns: X, X